root cause detection of call drops using feedforward neural network

Industrial Engineering Letters www.iiste.org

ISSN 2224-6096 (print) ISSN 2225-0581 (online)

Vol 2, No.6, 2012

25

Root cause detection of call drops using feedforward neural network

K R Sudhindra* V Sridhar

People’s Education Society College of Engineering, Mandya 571401, India

* E-mail of the corresponding author: [email protected]

Abstract

Call drop rate in GSM (Global System for Mobile Communication) network is an important key performance

indicator (KPI) that directly affects customer satisfaction. The delay in identification of exact call drop reason

because of multiple reasons involved in it would results in poor customer satisfaction. The TCH (traffic channel) call

drops due to three different hardware causes are collected from live GSM network for 10 days and are represented in

time domain. Time domain features such as mean, maximum, standard deviation etc. are extracted from each type of

call drop signal which is used to train the feedfoward neural network. FF neural network is made as decision making

classifier, feature vector is inputted and root cause detection information is outputted.

Keywords: TCH call drops, neural network, GSM

1. Introduction

TCH (Traffic channel) drop rate is one of the major KPI that affect the performance of live GSM network. The TCH

drop is the abrupt disconnection of call after traffic channel is allocated. The multiple causes of call drops in live

network will delay the process of call drop detection and its elimination from the network which will result in poor

customer satisfaction. The relation of call drops with handover and its effects on performance is exclusively

discussed in (Wahida Nasrin and Md Majharul Islam, 2009). The effect of user mobility on call drops in live GSM

network considering different patterns for user mobility was discussed in (A.G. Spilling and A.R. Nix, 2000). The

influence on handover failures on TCH call drops for different types of calls are discussed in (D.Lam, D.C Cox and

J.Widom,1997).In [A. Kolonits,1997] the lognormal hypothesis for distribution of the call holding time of both the

normally terminated and the abnormal dropped calls has been studied. The phenomena of TCH call drops have been

classified, verifying that handover failure become negligible in a well-established cellular network. All the previous

works implicitly assumes that proper radio planning has been done and there is no equipment failure or network

outage. In live network there are multiple causes for call drops identifying of which requires rich hands on

experience on the network. In many cases root cause detection of call drops will consume lot of time which results in

customer dissatisfaction. A novel method of root cause detection of TCH call drops using artificial neural network is

discussed in this paper.

2. Methodology

Root cause detection of TCH call drop based on feed forward (FF) neural network using Levenberg-Marquardt

training algorithm is designed. The block diagram of proposed system is shown in Figure 1. The TCH call drop

trends due to three different hardware causes are collected for 10 days and are represented in time domain. The next

step is to extract features from the signal representing TCH call drops and construct eigenvector for each cause using

extracted features. FF neural network is made as decision making classifier, signal eigenvector is inputted and root

cause detection information is outputted.



Vol 2, No.6, 2012

26

Figure 1. Block diagaram of root cause detection of TCH call drops

2.1Time domain representation of TCH call drops

The three major BTS hardware faults such as HDLC (High Level Data Link Control) communication between CMB

(control and maintenance board) and FUC(frame unit control) broken, Abis control link broken alarm and PA(Power

Amplifier) forward Power (3 db) alarm contributed for call drops in live network are considered for study in the

proposed system. The TCH call drops due to three different causes are collected for duration of 10 days with a

sampling time of 15 minutes and are represented in time domain as shown in Figure 2 to Figure 4. The time domain

representation TCH call drops shows unique characteristics for different hardware faults which are significant

finding that is used for feature extraction required for root cause identification. These data is used as input for

proposed root cause detection system.

Figure 2. HDLC Communication between CMB and FUC broken



Vol 2, No.6, 2012

27

Figure 3. Abis control link broken alarm

Figure 4. PA Forward Power (3 db) alarm

2.2 Feature Extraction

Five feature parameters such as mean, minimum, maximum, standard deviation, variance and signal power are

determined for each signal sample and standard feature vector is constructed for each fault type. Euclidean distance

of every two feature vectors can be calculated with the Euclidean distance formula and then compare the size of the

Euclidean distances. If Euclidean distances are significantly different and balanced between them, then feature

vectors are ideal (Yanhua Zhang and Lu Yang, 2010). These feature vectors are used for fault detection.



Vol 2, No.6, 2012

28

2.3 Root cause detection of call drops using feedforward neural network

Three layer feedforward artificial neural network (ANN) which is used in the proposed model is discussed in this

section. Computation nodes are arranged in layers and information feeds forward from layer to layer via weighted

connections as illustrated in Figure 5. Circles represent computation nodes (transfer functions), and lines represent

weighted connections. The bias threshold nodes are represented by squares. Mathematically, the typical feedforward

network can be expressed as shown in equation (1).

( )[ ]ohihoi bbBuCy ++ΦΦ= (1)

Figure 5. Three layer feed forward neural network

Where yi is the output vector corresponding to input vector ui , C is the connection matrix ( matrix of weights)

represented by arcs( the lines between two nodes) from the hidden layer to the output layer. B is the connection

matrix from the input layer to the hidden layer, and bh and bo are the bias vector for the hidden and output layer,

respectively, Φh (·) and Φo (·) are the vector valued function corresponding to the activation(transfer) functions of

the nodes in the hidden and output layers, respectively. Thus, feedforward neural network models have the general

structure of equation (2).

)(ufyi =

(2)

where f(·) is a nonlinear mapping. The continuous activation functions allow for the gradient based training of

multilayer networks [.K. Mohamad, S. Saon, M.H. Abd Wahab et al., 2008]. Various learning algorithms were

developed and only a few are suitable for multilayer neuron networks. Levenberg-Marquardt (LM) (Magali R. G.

Meirele and Paulo E. M. Almeida, 2003) learning is used in the proposed model of root cause detection of call drops.

TCH call drops due to three types of causes are collected for 10 days from OMC and used to construct feature vector

for training the neural network. Six unique group of feature vector from each type of signal are constructed. 18

groups of data that are obtained are used as training sample to be inputted into network to train the network. In

addition feature vectors are also constructed as detecting sample to test whether the network is working as per

design.

The specific structure of FF neural network consist ‘15’ neurons at hidden layer and ‘3’ neurons at output layer.

Hyperbolic secant S-transfer function “tansig” is adopted as transfer function of hidden layer and linear transfer



Vol 2, No.6, 2012

29

function “purelin” is adopted as transfer function of output layer. Levenberg-Marquardt BP training function is

adopted as network training function whose performance index is “mse” and training target is 0.01. After training,

the neural network can be given problems that are similar to the ones that it was trained on and it would make

decisions about the data that it is currently processing.

3. Results and discussion

Five feature parameters such as mean, maximum, standard deviation, variance and signal power are found using

TCH call drop time series signal and used as feature vector for fault detection. Table 1 shows the characteristics

parameters of TCH call drop time series signal.

Table 1. Characteristics parameters of TCH call drop time series signal

Sl.

No. Fault Type Mean Max Std Var Power

1

HDLC

Communication

between CMB and

FUC broken

0.97 34.00 3.10 10.00 13

2 Abis Control link

broken 0.59 14.00 1.14 1.13 0.93

3 PA forward power (3

dB) alarm 0.62 23.00 1.60 2.60 2.48

Root cause codes for HDLC communication between CMB and FUC broken (type1), Abis control link broken alarm

(type2) and PA forward Power (3 db) faults (type 3) are designed in Table 2. Part of training samples is shown in

Table 3. LM algorithm is used to train the feed forward neural network. Network training error curve is shown in

figure 6.

Table 2. Fault type code design

Parameters Fault Types

Type-1 Type-2 Type-3

Flaw codes 001 010 100



Vol 2, No.6, 2012

30

Table 3. Training Sample

Fault

Types

Input Vectors Fault codes

for input

vector U1 U2 U3 U4 U5

Type-1 0.971 34 3.107 9.476 13.780 100

Type-1 1.060 42 1.484 3.534 7.070 100

Type-1 1.822 13 3.077 9.473 20.803 100

Type-1 1.414 20 3.280 10.790 25.94 100

Type-1 0.945 32 3.201 10.24 11.66 100

Type-1 1.240 51 3.801 14.400 13 100

Type-2 0.589 14 0.934 1.140 0.93 010

Type-2 0.523 16 1.260 1.611 1.128 010

Type-2 0.714 17 1.618 2.618 2.123 010

Type-2 0.669 17 1.223 1.49 1.180 010

Type-2 0.228 7 0.681 0.464 0.473 010

Type-2 0.363 13 1.013 1.021 0.534 010

Type-3 0.514 59 2.078 4.319 4.120 001

Type-3 0.547 22 1.446 2.091 1.648 001

Type-3 1.036 21 2.439 6.041 9.610 001

Type-3 1.170 21 2.144 4.591 6.840 001

Type-3 0.417 23 1.700 2.653 2.482 001

Type-3 0.640 10 1.130 1.270 1.96 001

Figure 6. Train Error curve

From Figure 6 we observed that final mean-square error is small, the test set error and the validation set error have

similar characteristics and no significant overfitting has occurred by iteration ‘3’ where the best validation

performance occurs. In order to verify the accuracy of network, test samples with a total of ‘9’ sets of data are used to

test network model and test results are shown in table 4. From table 4 it is found that the actual output of network is

accordance with expectation output.



Vol 2, No.6, 2012

31

Table 4 Sample test results

Fault

Types

Input Vectors Expected

outputs

Actual

outputs Results

U1 U2 U3 U4 U5

Type-1 0.9 34 3.1 10 13 100 0.9992 -0.0003

-

0.0004 correct

Type-1 0.8 32 3.1 9 12 100 0.9994 -0.0003

-

0.0003 correct

Type-1 0.7 28 2.7 12 13 100 0.9999 -0.0001 0.0002 correct

Type-2 0.5 14 0.14 1.13 0.93 010 -0.0268 0.8323 0.1885 correct

Type-2 0.6 12 1.12 2.87 1.12 010 0.0013 1.0014 0.0005 correct

Type-2 0.4 13 0.13 2.14 4.12 010 0.0008 1.0023 -0.002 correct

Type-3 0.6 23 1.57 3.61 2.48 010 0.0019 0.0012 0.9997 correct

Type-3 0.7 22 1.63 2.7 2.48 010 0.0020 0.0015 1.0000 correct

Type-3 0.6 24 1.61 4.3 2.23 010 -0.0009 -0.1082 1.1023 correct

4. Conclusions

The time series representation of TCH call drops shows unique characteristics for different hardware faults. These

characteristics help to extract time domain features and construct Eigen vector for identifying root cause of call

drops. Root cause detector of TCH call drops using feedforward neural network is designed and LM algorithm is

used to train the network from the constructed feature vectors. The efficiency of the network can be improved by

training the network with large number of samples.

5. Acknowledgment

The authors would like to thank IDEA Cellular Ltd, Bangalore to have made possible the access to the data used for

this study.

References

Wahida Nasrin, Md Majharul Islam Rajib,“An analytical approach to enhance the capacity of GSM frequency

hopping networks with intelligent Underlay-overlay” Journal of communication, Vol 4,No. 6, July 2009.

A.G. Spilling and A.R. Nix, “Performance enhancement in cellular networks with dynamic cell sizing” IEEE PIMRC

2000.

D.Lam,D.C Cox and J.Widom “Teletraffic modeling for personal communication services” IEEE communications

Magazine, Vol. 35, No. 2, Feb 1997,pp 79-87.

A. Kolonits, “Evaluating the Potential of Multiple Re-Use Patterns for Optimizing Existing Network Capacity” IIR

Maximizing Capacity Workshop, London, June 1997.

Yanhua Zhang, Lu Yang et al., “ Study of feature extraction and classification of ultrasonic flaw signals” WSEAS

Trans. On Mathematics, issue 7, Vol. 9, July 2010.

A.K. Mohamad, S. Saon, M.H. Abd Wahab et al.,” Using Artificial Neural Network to monitor and predict induction

motor bearing (IMB) failure”International Engineering Convention, Jeddah, Saudi Arabia, 10-14, March, 2008.

Magali R. G. Meireles, Paulo E. M. Almeida et al. “A Comprehensive Review for Industrial Applicability of

Artificial Neural Networks” IEEE Tran. on Industrial Electronics, Vol. 50. NO. 3, June 2003, 585.



Vol 2, No.6, 2012

32

K R Sudhindra received Bachelor of Engineering degree in Electroncs and communication from Mysore University,

India in 1999 and M.Sc ( Engg). by research in faculty of Electrical Engineering sciences from Visvesvaraya

Technological University, India in 2007. He is currently a Ph.D student of Department of Electronics and

Communication Engineering, PESCE, Karnataka, India. He has total 5 years of experience in Telecom Industry. His

research interests include operational research, signal processing & wireless communication.

V Sridhar has obtained his Ph.D from Indian Institute of Technology (IITD), New Delhi in the year 1996. He

obtained his B.E (E&C) from University of Mysore in the year 1980 and M.E (Electronics & Telecommunications)

from Jadavpur university, Calcutta in the year 1986. Presently he is serving as the Principal, PESCE, Mandya. He has

more than 29 years of teaching, research and administrative experience.His major areas of research interest are Bio-

medical instrumentation, Telemedicine, VLSI Design and Mobile communication. He has to his credit more than 40

research papers in national /international journals and conferences.

This academic article was published by The International Institute for Science,

Technology and Education (IISTE). The IISTE is a pioneer in the Open Access

Publishing service based in the U.S. and Europe. The aim of the institute is

Accelerating Global Knowledge Sharing.

More information about the publisher can be found in the IISTE’s homepage:

http://www.iiste.org

The IISTE is currently hosting more than 30 peer-reviewed academic journals and

collaborating with academic institutions around the world. Prospective authors of

IISTE journals can find the submission instruction on the following page:

http://www.iiste.org/Journals/

The IISTE editorial team promises to the review and publish all the qualified

submissions in a fast manner. All the journals articles are available online to the

readers all over the world without financial, legal, or technical barriers other than

those inseparable from gaining access to the internet itself. Printed version of the

journals is also available upon request of readers and authors.

IISTE Knowledge Sharing Partners

EBSCO, Index Copernicus, Ulrich's Periodicals Directory, JournalTOCS, PKP Open

Archives Harvester, Bielefeld Academic Search Engine, Elektronische

Zeitschriftenbibliothek EZB, Open J-Gate, OCLC WorldCat, Universe Digtial

Library , NewJour, Google Scholar

http://www.iiste.org/

http://www.iiste.org/Journals/